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Abstract #1964

MR-Class: MR Image Classification using one-vs-all Deep Convolutional Neural Network

Patrick Salome1,2,3,4, Francesco Sforazzini1,2,3,4, Andreas Kudak3,5,6, Matthias Dostal3,5,6, Nina Bougatf3,5,6, Jürgen Debus3,4,5,7, Amir Abdollahi1,3,4,5, and Maximilian Knoll1,3,4,5
1CCU Translational Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 2Medical Faculty, Heidelberg University Hospital, Heidelberg, Germany, 3Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg, Germany, 4German Cancer Consortium (DKTK) Core Center, Heidelberg, Germany, 5Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany, 6CCU Radiation Therapy, German Cancer Research Center (DKFZ), Heidelberg, Germany, 7National Center for Tumor Diseases (NCT), Heidelberg, Germany


MR-Class is a deep learning-based MR image classification tool that facilitates and speeds up the initialization of big data MR-based research studies by providing fast, robust, and quality-assured MR image classifications. It was observed in this study that corrupt and misleading DICOM metadata could lead to a misclassification of about 10%. Therefore, in a field where independent datasets are frequently needed for study validations, MR-Class can eliminate the cumbrousness of data cohorts curation and sorting. This can greatly impact researchers interested in big data multiparametric MRI studies and thus contribute to the faster deployment of clinical artificial intelligence applications.

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